Feature Extraction
Transformers
Safetensors
PyTorch
English
spatial-transcriptomics
graph-transformer
gene-expression
finetuned
mouse-stroke
Instructions to use Bgoood/SpatialGT-MouseStroke-Sham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bgoood/SpatialGT-MouseStroke-Sham with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Bgoood/SpatialGT-MouseStroke-Sham")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bgoood/SpatialGT-MouseStroke-Sham", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 190d0f97ab2aa073986ecc0657e9daeafe8f176fd732b5137422e702bdfca4ab
- Size of remote file:
- 5.43 kB
- SHA256:
- 6fa13c00471daafd8b1afcccdea37c6014326fbc421e3d89758aef0c17e9f7c5
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